class_docstr: Head which outputs a mixture of multivariate Gaussians distribution.
docstr: Initialization.
  
  Args:
    num_dimensions: dimensionality of the output distribution
      (also the dimensionality of the multivariate Gaussian model).
    num_components: number of mixture components.
    init_scale: the initial scale for the Gaussian mixture components.
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code: def __init__(
      self,
      num_dimensions: int,
      num_components: int = 5,
      init_scale: Optional[float] = None,
  ):
      """Initialization.
  
      Args:
        num_dimensions: dimensionality of the output distribution
          (also the dimensionality of the multivariate Gaussian model).
        num_components: number of mixture components.
        init_scale: the initial scale for the Gaussian mixture components.
      """
      super().__init__(
          num_dimensions=num_dimensions,
          num_components=num_components,
          multivariate=True,
          init_scale=init_scale,
          name="MultivariateGaussianMixture",
      )
  
imports: import types
  from typing import Optional, Union
  from absl import logging
  from acme.tf.networks import distributions
  import numpy
  import sonnet
  import tensorflow
  import tensorflow_probability
label_desc: Use image node_id 1 to create a new MultivariateGaussianMixture object from inherited base classes: GaussianMixture with example: obj = MultivariateGaussianMixture(num_dimensions, num_components, init_scale)